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ORCIDCynthia Rudin
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- affiliation: Duke University, USA
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2020 – today
- 2024
[j52]Manickam Ashokkumar
, Wenwen Mei, Jackson J. Peterson, Yuriko Harigaya, David M. Murdoch, David M. Margolis, Caleb Kornfein, Alexander X. Oesterling, Zhicheng Guo, Cynthia Rudin, Yuchao Jiang, Edward P. Browne:
Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. Genom. Proteom. Bioinform. 22(1) (2024)- [j51]Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin:
Sparse learned kernels for interpretable and efficient medical time series processing. Nat. Mac. Intell. 6(10): 1132-1144 (2024) - [j50]Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Amit J. Shah, Duc H. Do, Randall J. Lee, Gari D. Clifford, Fadi B. Nahab, Xiao Hu:
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms. IEEE J. Biomed. Health Informatics 28(5): 2650-2661 (2024) - [c93]Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference. AAAI 2024: 22331-22340 - [c92]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Survival Trees. AISTATS 2024: 352-360 - [c91]Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin:
Sparse and Faithful Explanations Without Sparse Models. AISTATS 2024: 2071-2079 - [c90]Harsh Parikh, Quinn Lanners, Zade Akras, Sahar Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Safe and Interpretable Estimation of Optimal Treatment Regimes. AISTATS 2024: 2134-2142 - [c89]Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. AISTATS 2024: 3340-3348 - [c88]Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin:
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. CVPR Workshops 2024: 5003-5009 - [c87]Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner:
Position: Amazing Things Come From Having Many Good Models. ICML 2024 - [c86]Stephen Hahn, Jerry Yin, Rico Zhu, Weihan Xu, Yue Jiang, Simon Mak, Cynthia Rudin:
SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization. KDD 2024: 5050-5060 - [c85]Jiachang Liu, Rui Zhang, Cynthia Rudin:
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models. NeurIPS 2024 - [c84]Haiyang Huang, Yingfan Wang, Cynthia Rudin:
Navigating the Effect of Parametrization for Dimensionality Reduction. NeurIPS 2024 - [c83]Zachery Boner, Harry Chen, Lesia Semenova, Ronald Parr, Cynthia Rudin:
Using Noise to Infer Aspects of Simplicity Without Learning. NeurIPS 2024 - [c82]Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen:
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. NeurIPS 2024 - [c81]Hayden McTavish, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin:
Interpretable Generalized Additive Models for Datasets with Missing Values. NeurIPS 2024 - [c80]Yiyang Sun, Tong Wang, Cynthia Rudin:
Improving Decision Sparsity. NeurIPS 2024 - [i114]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Survival Trees. CoRR abs/2401.15330 (2024) - [i113]Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin:
Sparse and Faithful Explanations Without Sparse Models. CoRR abs/2402.09702 (2024) - [i112]Varun Babbar, Zhicheng Guo, Cynthia Rudin:
What is different between these datasets? CoRR abs/2403.05652 (2024) - [i111]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. CoRR abs/2404.04714 (2024) - [i110]Cheng Ding, Zhicheng Guo, Zhaoliang Chen, Randall J. Lee, Cynthia Rudin, Xiao Hu:
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals. CoRR abs/2404.17667 (2024) - [i109]Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin:
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. CoRR abs/2406.06386 (2024) - [i108]Frank Willard, Luke Moffett, Emmanuel Mokel, Jon Donnelly, Stark Guo, Julia Yang, Giyoung Kim, Alina Jade Barnett, Cynthia Rudin:
This Looks Better than That: Better Interpretable Models with ProtoPNeXt. CoRR abs/2406.14675 (2024) - [i107]Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner:
Amazing Things Come From Having Many Good Models. CoRR abs/2407.04846 (2024) - [i106]Stephen Ni-Hahn, Weihan Xu, Jerry Yin, Rico Zhu, Simon Mak, Yue Jiang, Cynthia Rudin:
A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis. CoRR abs/2408.07184 (2024) - [i105]Mary Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio, Cynthia Rudin, L. Catherine Brinson:
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning. CoRR abs/2408.08428 (2024) - [i104]Jiachang Liu, Rui Zhang, Cynthia Rudin:
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models. CoRR abs/2410.19081 (2024) - [i103]Yiyang Sun, Tong Wang, Cynthia Rudin:
Improving Decision Sparsity. CoRR abs/2410.20483 (2024) - [i102]Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen:
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. CoRR abs/2410.20722 (2024) - [i101]Haiyang Huang, Yingfan Wang, Cynthia Rudin:
Navigating the Effect of Parametrization for Dimensionality Reduction. CoRR abs/2411.15894 (2024) - [i100]Hayden McTavish, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin:
Interpretable Generalized Additive Models for Datasets with Missing Values. CoRR abs/2412.02646 (2024) - [i99]Yingfan Wang, Yiyang Sun, Haiyang Huang, Cynthia Rudin:
Dimension Reduction with Locally Adjusted Graphs. CoRR abs/2412.15426 (2024) - 2023
- [j49]Lina Zhou, Cynthia Rudin, Matthew C. Gombolay, Jim Spohrer, Michelle Zhou, Souren Paul:
From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues. AIS Trans. Hum. Comput. Interact. 15(1): 111-135 (2023) - [j48]Cynthia Rudin, Yaron Shaposhnik:
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. J. Mach. Learn. Res. 24: 16:1-16:44 (2023) - [c79]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Regression Trees. AAAI 2023: 11270-11279 - [c78]Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, Cynthia Rudin:
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. ACL (2) 2023: 1627-1638 - [c77]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL 2023: 86-99 - [c76]Stephen Hahn, Rico Zhu, Simon Mak, Cynthia Rudin, Yue Jiang:
An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. KDD 2023: 4089-4099 - [c75]Yanchen Jessie Ou, Alina Jade Barnett, Anika Mitra, Fides Regina Schwartz, Chaofan Chen, Lars J. Grimm, Joseph Y. Lo, Cynthia Rudin:
A user interface to communicate interpretable AI decisions to radiologists. Image Perception, Observer Performance, and Technology Assessment 2023 - [c74]Yanchen Ou, Alina Jade Barnett, Anika Mitra, Fides Regina Schwartz, Chaofan Chen, Lars J. Grimm, Joseph Y. Lo, Cynthia Rudin:
A user interface to communicate interpretable AI decisions to radiologists (Erratum). Image Perception, Observer Performance, and Technology Assessment 2023 - [c73]Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin:
OKRidge: Scalable Optimal k-Sparse Ridge Regression. NeurIPS 2023 - [c72]Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne:
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. NeurIPS 2023 - [c71]Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin:
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. NeurIPS 2023 - [c70]Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. NeurIPS 2023 - [c69]Chudi Zhong, Zhi Chen, Jiachang Liu, Margo I. Seltzer, Cynthia Rudin:
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models. NeurIPS 2023 - [c68]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
Variable importance matching for causal inference. UAI 2023: 1174-1184 - [i98]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. CoRR abs/2302.11715 (2023) - [i97]Zhi Chen, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models. CoRR abs/2303.16047 (2023) - [i96]Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin:
OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems. CoRR abs/2304.06686 (2023) - [i95]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - [i94]Marco Morucci, Vittorio Orlandi, Harsh Parikh, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
A Double Machine Learning Approach to Combining Experimental and Observational Data. CoRR abs/2307.01449 (2023) - [i93]Pranay Jain, Cheng Ding, Cynthia Rudin, Xiao Hu:
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables. CoRR abs/2307.05339 (2023) - [i92]Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin:
Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation. CoRR abs/2307.05385 (2023) - [i91]Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne:
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. CoRR abs/2309.13775 (2023) - [i90]Zhicheng Guo, Cheng Ding, Duc H. Do, Amit J. Shah, Randall J. Lee, Xiao Hu, Cynthia Rudin:
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection. CoRR abs/2310.09203 (2023) - [i89]Han Zhang, Rayehe Karimi Mahabadi, Cynthia Rudin, Johann Guilleminot, L. Catherine Brinson:
Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties. CoRR abs/2310.12869 (2023) - [i88]Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Estimating Trustworthy and Safe Optimal Treatment Regimes. CoRR abs/2310.15333 (2023) - [i87]Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin:
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. CoRR abs/2310.18589 (2023) - [i86]Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. CoRR abs/2310.19726 (2023) - [i85]Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin:
Fast and Interpretable Mortality Risk Scores for Critical Care Patients. CoRR abs/2311.13015 (2023) - [i84]Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Fadi B. Nahab, Xiao Hu:
Reconsideration on evaluation of machine learning models in continuous monitoring using wearables. CoRR abs/2312.02300 (2023) - [i83]Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, M. Brandon Westover:
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges. CoRR abs/2312.10056 (2023) - [i82]Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. CoRR abs/2312.10569 (2023) - 2022
- [j47]Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decis. Support Syst. 152: 113647 (2022) - [j46]Tong Wang, Cynthia Rudin:
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. INFORMS J. Comput. 34(3): 1626-1643 (2022) - [j45]Chunxiao Li, Cynthia Rudin, Tyler H. McCormick:
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. J. Mach. Learn. Res. 23: 96:1-96:55 (2022) - [j44]Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
MALTS: Matching After Learning to Stretch. J. Mach. Learn. Res. 23: 240:1-240:42 (2022) - [c67]Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer:
Fast Sparse Decision Tree Optimization via Reference Ensembles. AAAI 2022: 9604-9613 - [c66]Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. AISTATS 2022: 9304-9333 - [c65]Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Mango I. Seltzer:
Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. CIKM Workshops 2022 - [c64]Lesia Semenova, Cynthia Rudin, Ronald Parr:
On the Existence of Simpler Machine Learning Models. FAccT 2022: 1827-1858 - [c63]Alina Jade Barnett, Vaibhav Sharma, Neel Gajjar, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin:
Interpretable deep learning models for better clinician-AI communication in clinical mammography. Image Perception, Observer Performance, and Technology Assessment 2022 - [c62]Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. NeurIPS 2022 - [c61]Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. NeurIPS 2022 - [c60]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data poisoning attacks on off-policy policy evaluation methods. UAI 2022: 1264-1274 - [c59]Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer:
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. IEEE VIS (Short Papers) 2022: 60-64 - [i81]Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. CoRR abs/2202.11389 (2022) - [i80]Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover:
Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It. CoRR abs/2203.04920 (2022) - [i79]Haiyang Huang, Zhi Chen, Cynthia Rudin:
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation. CoRR abs/2204.10926 (2022) - [i78]Yishay Mansour, Michal Moshkovitz, Cynthia Rudin:
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. CoRR abs/2206.04266 (2022) - [i77]Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. CoRR abs/2209.08040 (2022) - [i76]Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer:
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. CoRR abs/2209.09227 (2022) - [i75]Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. CoRR abs/2210.05846 (2022) - [i74]Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo I. Seltzer:
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. CoRR abs/2210.06825 (2022) - [i73]Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover:
Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. CoRR abs/2211.05207 (2022) - [i72]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Regression Trees. CoRR abs/2211.14980 (2022) - 2021
- [j43]Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin:
Playing Codenames with Language Graphs and Word Embeddings. J. Artif. Intell. Res. 71: 319-346 (2021) - [j42]Stefano Tracà, Cynthia Rudin, Weiyu Yan:
Regulating Greed Over Time in Multi-Armed Bandits. J. Mach. Learn. Res. 22: 3:1-3:99 (2021) - [j41]Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. J. Mach. Learn. Res. 22: 31:1-31:41 (2021) - [j40]Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik:
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization. J. Mach. Learn. Res. 22: 201:1-201:73 (2021) - [j39]Beau Coker, Cynthia Rudin, Gary King:
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Manag. Sci. 67(10): 6174-6197 (2021) - [j38]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat. Mach. Intell. 3(12): 1061-1070 (2021) - [j37]Jianyou Wang, Cynthia Rudin, Yuren Zhou, Christopher Suh, Xiaoxuan Zhang:
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. Trans. Assoc. Comput. Linguistics 9: 605-620 (2021) - [c58]Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. AIES 2021: 316-326 - [d1]Alexander C. Ogren, Zhi Chen, L. Catherine Brinson, Mary Bastawrous, Cynthia Rudin, Chiara Daraio:
2D elastodynamic metamaterials. UCI Machine Learning Repository, 2021
- [i71]Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference. CoRR abs/2101.01867 (2021) - [i70]Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin:
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. CoRR abs/2103.03775 (2021) - [i69]Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong:
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. CoRR abs/2103.11251 (2021) - [i68]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. CoRR abs/2103.12308 (2021) - [i67]Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. CoRR abs/2105.00060 (2021) - [i66]Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin:
Playing Codenames with Language Graphs and Word Embeddings. CoRR abs/2105.05885 (2021) - [i65]Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations. CoRR abs/2106.02605 (2021) - [i64]Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin:
Multitask Learning for Citation Purpose Classification. CoRR abs/2106.13275 (2021) - [i63]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. CoRR abs/2107.05605 (2021) - [i62]Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin:
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales. CoRR abs/2109.07623 (2021) - [i61]Zhi Chen, Alexander C. Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin:
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. CoRR abs/2111.05949 (2021) - [i60]Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer:
Fast Sparse Decision Tree Optimization via Reference Ensembles. CoRR abs/2112.00798 (2021) - 2020
- [j36]Alexander S. Rich, Cynthia Rudin, David M. P. Jacoby, Robin Freeman, Oliver R. Wearn, Henry Shevlin, Kanta Dihal, Seán S. ÓhÉigeartaigh, James Butcher, Marco Lippi, Przemyslaw Palka, Paolo Torroni, Shannon Wongvibulsin, Edmon Begoli, Gisbert Schneider, Stephen Cave, Mona Sloane, Emanuel Moss, Iyad Rahwan, Ken Goldberg, David Howard, Luciano Floridi, Jack Stilgoe:
AI reflections in 2019. Nat. Mach. Intell. 2(1): 2-9 (2020) - [j35]Zhi Chen, Yijie Bei, Cynthia Rudin:
Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2(12): 772-782 (2020) - [j34]Jiayun Dong, Cynthia Rudin:
Exploring the cloud of variable importance for the set of all good models. Nat. Mach. Intell. 2(12): 810-824 (2020) - [c57]Jerry Liu, Nathan O'Hara, Alexander Rubin, Rachel Lea Draelos, Cynthia Rudin:
Metaphor Detection Using Contextual Word Embeddings From Transformers.